Object detection in thermal infrared spectrum provides more reliable data source in low-lighting conditions and different weather conditions, as it is useful both in-cabin and outside for pedestrian, animal, and vehicular detection as well as for detecting street-signs & lighting poles. This paper is about exploring and adapting state-of-the-art object detection and classifier framework on thermal vision with seven distinct classes for advanced driver-assistance systems (ADAS). The trained network variants on public datasets are validated on test data with three different test approaches which include test-time with no augmentation, test-time augmentation, and test-time with model ensembling. Additionally, the efficacy of trained networks is tested on locally gathered novel test-data captured with an uncooled LWIR prototype thermal camera in challenging weather and environmental scenarios. The performance analysis of trained models is investigated by computing precision, recall, and mean average precision scores (mAP). Furthermore, the trained model architecture is optimized using TensorRT inference accelerator and deployed on resource-constrained edge hardware Nvidia Jetson Nano to explicitly reduce the inference time on GPU as well as edge devices for further real-time onboard installations.
翻译:热红外线频谱中热红外线天体探测为低光度条件和不同天气条件下提供更可靠的数据源,因为它对行人、动物和车辆探测以及探测路标和照明杆有用,因此在闭路电视和外部对行人、动物和车辆探测以及探测路标和照明杆都有用。本文涉及探索和修改热视最先进的物体探测和分类框架,为高级助动系统(ADAS)分为七个不同的类别。经过培训的公共数据集网络变体根据测试数据验证,采用三种不同的测试方法,包括没有增强、测试时间增强的测试时间和模型结合的测试时间。此外,对经过培训的网络的功效进行测试,利用当地收集的新测试测试的测试数据,用不冷的LWIR原型热相机在挑战天气和环境的情况下采集。经过培训的模型的性能分析是通过计算精确度、回顾和平均平均精确分数(MAP)来调查的。此外,经过培训的模型结构正在优化,使用TensorRT的推力加速器进行优化,并安装在受资源限制的边缘硬件Nvidia Jetson Nano Nano的纳米系统,以进一步降低地面装置的边缘。